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Amusat O, Atia AA, Dudchenko AV, Bartholomew TV. Modeling Framework for Cost Optimization of Process-Scale Desalination Systems with Mineral Scaling and Precipitation. ACS ES&T ENGINEERING 2024; 4:1028-1047. [PMID: 38751651 PMCID: PMC11091887 DOI: 10.1021/acsestengg.3c00537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 01/26/2024] [Accepted: 01/30/2024] [Indexed: 05/18/2024]
Abstract
Cost-optimization models are powerful tools for evaluating emerging water treatment processes. However, to date, optimization models do not incorporate detailed chemical reaction phenomena, limiting the assessment of pretreatment and mineral scaling. Moreover, novel approaches for high-salinity and high-recovery desalination are typically proposed without direct quantification of pretreatment needs or mineral scaling. This work addresses a critical gap in the literature by presenting a modeling framework that includes complex water chemistry predictions with process-scale optimization. We use this approach to conduct a technoeconomic assessment on a conceptual high-recovery treatment train that includes chemical pretreatment (i.e., soda ash softening and recarbonation) and membrane-based desalination (i.e., standard and high-pressure reverse osmosis). We demonstrate how to develop and integrate accurate multidimensional surrogate models for predicting precipitation, pH, and mineral scaling tendencies. Our findings show that cost-optimal results balance the costs of pretreatment with reverse osmosis system design. Optimizing across a range of water recoveries (i.e., 50-90%) reveals multiple cost-optimal schemas that vary the chemical dosing in pretreatment and the design and operation of reverse osmosis. Our results reveal that pretreatment costs can be more than double the cost of the primary desalination process at high recoveries due to the extensive pretreatment required to control scaling. This work emphasizes the importance of and provides a framework for including chemistry and mineral scaling predictions in the evaluation of emerging technologies in high-recovery desalination.
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Affiliation(s)
- Oluwamayowa
O. Amusat
- Lawrence
Berkeley National Laboratory (LBNL), 1 Cyclotron Road, Berkeley, California 94720, United States
| | - Adam A. Atia
- National
Energy Technology Laboratory (NETL), Pittsburgh, Pennsylvania 15236, United States
- NETL
Support Contractor, Pittsburgh, Pennsylvania 15236, United States
| | - Alexander V. Dudchenko
- SLAC
National Accelerator Laboratory, 2575 Sand Hill Road, Menlo
Park, California 94025, United States
| | - Timothy V. Bartholomew
- National
Energy Technology Laboratory (NETL), Pittsburgh, Pennsylvania 15236, United States
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2
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Samadi ME, Mirzaieazar H, Mitsos A, Schuppert A. Noisecut: a python package for noise-tolerant classification of binary data using prior knowledge integration and max-cut solutions. BMC Bioinformatics 2024; 25:155. [PMID: 38641616 PMCID: PMC11031902 DOI: 10.1186/s12859-024-05769-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Accepted: 04/09/2024] [Indexed: 04/21/2024] Open
Abstract
BACKGROUND Classification of binary data arises naturally in many clinical applications, such as patient risk stratification through ICD codes. One of the key practical challenges in data classification using machine learning is to avoid overfitting. Overfitting in supervised learning primarily occurs when a model learns random variations from noisy labels in training data rather than the underlying patterns. While traditional methods such as regularization and early stopping have demonstrated effectiveness in interpolation tasks, addressing overfitting in the classification of binary data, in which predictions always amount to extrapolation, demands extrapolation-enhanced strategies. One such approach is hybrid mechanistic/data-driven modeling, which integrates prior knowledge on input features into the learning process, enhancing the model's ability to extrapolate. RESULTS We present NoiseCut, a Python package for noise-tolerant classification of binary data by employing a hybrid modeling approach that leverages solutions of defined max-cut problems. In a comparative analysis conducted on synthetically generated binary datasets, NoiseCut exhibits better overfitting prevention compared to the early stopping technique employed by different supervised machine learning algorithms. The noise tolerance of NoiseCut stems from a dropout strategy that leverages prior knowledge of input features and is further enhanced by the integration of max-cut problems into the learning process. CONCLUSIONS NoiseCut is a Python package for the implementation of hybrid modeling for the classification of binary data. It facilitates the integration of mechanistic knowledge on the input features into learning from data in a structured manner and proves to be a valuable classification tool when the available training data is noisy and/or limited in size. This advantage is especially prominent in medical and biomedical applications where data scarcity and noise are common challenges. The codebase, illustrations, and documentation for NoiseCut are accessible for download at https://pypi.org/project/noisecut/ . The implementation detailed in this paper corresponds to the version 0.2.1 release of the software.
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Affiliation(s)
- Moein E Samadi
- Institute for Computational Biomedicine, RWTH Aachen University, Aachen, Germany
| | - Hedieh Mirzaieazar
- Institute for Computational Biomedicine, RWTH Aachen University, Aachen, Germany
| | - Alexander Mitsos
- Process Systems Engineering (AVT.SVT), RWTH Aachen University, Aachen, Germany
| | - Andreas Schuppert
- Institute for Computational Biomedicine, RWTH Aachen University, Aachen, Germany.
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Krüger M, Mishra A, Spichtinger P, Pöschl U, Berkemeier T. A numerical compass for experiment design in chemical kinetics and molecular property estimation. J Cheminform 2024; 16:34. [PMID: 38520014 PMCID: PMC10960421 DOI: 10.1186/s13321-024-00825-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 03/10/2024] [Indexed: 03/25/2024] Open
Abstract
Kinetic process models are widely applied in science and engineering, including atmospheric, physiological and technical chemistry, reactor design, or process optimization. These models rely on numerous kinetic parameters such as reaction rate, diffusion or partitioning coefficients. Determining these properties by experiments can be challenging, especially for multiphase systems, and researchers often face the task of intuitively selecting experimental conditions to obtain insightful results. We developed a numerical compass (NC) method that integrates computational models, global optimization, ensemble methods, and machine learning to identify experimental conditions with the greatest potential to constrain model parameters. The approach is based on the quantification of model output variance in an ensemble of solutions that agree with experimental data. The utility of the NC method is demonstrated for the parameters of a multi-layer model describing the heterogeneous ozonolysis of oleic acid aerosols. We show how neural network surrogate models of the multiphase chemical reaction system can be used to accelerate the application of the NC for a comprehensive mapping and analysis of experimental conditions. The NC can also be applied for uncertainty quantification of quantitative structure-activity relationship (QSAR) models. We show that the uncertainty calculated for molecules that are used to extend training data correlates with the reduction of QSAR model error. The code is openly available as the Julia package KineticCompass.
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Affiliation(s)
- Matteo Krüger
- Multiphase Chemistry Department, Max Planck Institute for Chemistry, Hahn-Meitner-Weg 1, Mainz, 55128, Rhineland Palatinate, Germany
| | - Ashmi Mishra
- Multiphase Chemistry Department, Max Planck Institute for Chemistry, Hahn-Meitner-Weg 1, Mainz, 55128, Rhineland Palatinate, Germany
| | - Peter Spichtinger
- Institute for Atmospheric Physics, Johannes Gutenberg University, Johann-Joachim-Becher-Weg 21, Mainz, 55128, Rhineland Palatinate, Germany
| | - Ulrich Pöschl
- Multiphase Chemistry Department, Max Planck Institute for Chemistry, Hahn-Meitner-Weg 1, Mainz, 55128, Rhineland Palatinate, Germany
| | - Thomas Berkemeier
- Multiphase Chemistry Department, Max Planck Institute for Chemistry, Hahn-Meitner-Weg 1, Mainz, 55128, Rhineland Palatinate, Germany.
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Risal S, Singh N, Yao Y, Sun L, Risal S, Zhu W. Accelerating Elastic Property Prediction in Fe-C Alloys through Coupling of Molecular Dynamics and Machine Learning. MATERIALS (BASEL, SWITZERLAND) 2024; 17:601. [PMID: 38591477 PMCID: PMC10856267 DOI: 10.3390/ma17030601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 01/18/2024] [Accepted: 01/24/2024] [Indexed: 04/10/2024]
Abstract
The scarcity of high-quality data presents a major challenge to the prediction of material properties using machine learning (ML) models. Obtaining material property data from experiments is economically cost-prohibitive, if not impossible. In this work, we address this challenge by generating an extensive material property dataset comprising thousands of data points pertaining to the elastic properties of Fe-C alloys. The data were generated using molecular dynamic (MD) calculations utilizing reference-free Modified embedded atom method (RF-MEAM) interatomic potential. This potential was developed by fitting atomic structure-dependent energies, forces, and stress tensors evaluated at ground state and finite temperatures using ab-initio. Various ML algorithms were subsequently trained and deployed to predict elastic properties. In addition to individual algorithms, super learner (SL), an ensemble ML technique, was incorporated to refine predictions further. The input parameters comprised the alloy's composition, crystal structure, interstitial sites, lattice parameters, and temperature. The target properties were the bulk modulus and shear modulus. Two distinct prediction approaches were undertaken: employing individual models for each property prediction and simultaneously predicting both properties using a single integrated model, enabling a comparative analysis. The efficiency of these models was assessed through rigorous evaluation using a range of accuracy metrics. This work showcases the synergistic power of MD simulations and ML techniques for accelerating the prediction of elastic properties in alloys.
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Affiliation(s)
- Sandesh Risal
- Department of Mechanical Engineering, University of Houston, Houston, TX 77204, USA; (S.R.); (L.S.)
| | - Navdeep Singh
- Department of Mechanical Engineering, School of Engineering and Computer Science, University of the Pacific, Stockton, CA 95211, USA
| | - Yan Yao
- Materials Science and Engineering Program, University of Houston, Houston, TX 77204, USA; (Y.Y.); (S.R.)
- Department of Electrical and Computer Engineering, University of Houston, Houston, TX 77204, USA
| | - Li Sun
- Department of Mechanical Engineering, University of Houston, Houston, TX 77204, USA; (S.R.); (L.S.)
| | - Samprash Risal
- Materials Science and Engineering Program, University of Houston, Houston, TX 77204, USA; (Y.Y.); (S.R.)
| | - Weihang Zhu
- Department of Mechanical Engineering, University of Houston, Houston, TX 77204, USA; (S.R.); (L.S.)
- Department of Engineering Technology, University of Houston, Houston, TX 77204, USA
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Qian Q, Ren J. From plastic waste to potential wealth: Upcycling technologies, process synthesis, assessment and optimization. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 907:167897. [PMID: 37866600 DOI: 10.1016/j.scitotenv.2023.167897] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 10/02/2023] [Accepted: 10/16/2023] [Indexed: 10/24/2023]
Abstract
Global plastics production has doubled since the beginning of 21st century. Efficient technology is called for plastics waste valorization. The current review provides an overview of the main waste plastic chemical upcycling technologies to produce value-added products. Various technologies including gasification and pyrolysis are under reviewed. However, several review literatures have paid attention to the details and experimental progress in these chemical upcycling techniques. In this review, we attempt to conclude the progress in a multi-scale systems-by-systems perspective. After a brief overview of the current state-of-the-art chemical upcycling techniques, larger-scale process synthesis, assessment, and optimization methodologies to address the sustainability and environmental issues are summarized. Techno-economic analysis and life cycle assessment are selected as two powerful tools for process assessment. Three particular application scenarios of optimization methodologies including experimental design, process synthesis and supply chain management are consequently introduced. Very little work on review articles have summarized the plastic waste-to-wealth process in the systems engineering perspective. Review results show that (1) gasification and pyrolysis offer promising avenues for the conversion of plastic waste into valuable products. These technologies can be integrated with other subsystems to enhance the economic and environmental performance of the overall system. (2) Response surface methodology is commonly used in experimental design and parameter optimization. It allows researchers to systematically investigate the effects of various parameters and optimize process conditions to maximize desired outputs. (3) Superstructure optimization frameworks are valuable tools for process synthesis and pathway selection in plastic waste conversion. However, the potential superstructure is pre-defined. (4) Green supply chain and multi-objective supply chain frameworks can be applied to the design of plastic waste recycling networks, taking into account both economic and environmental considerations.
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Affiliation(s)
- Qiming Qian
- Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China
| | - Jingzheng Ren
- Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China.
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6
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Baldan M, Ludl PO, Süss P, Schack D, Schmidt R, Bortz M. Real‐Time Interactive Navigation on Input‐Output Data Sets in Chemical Processes. CHEM-ING-TECH 2023. [DOI: 10.1002/cite.202200240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
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7
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Rihm GB, Schueler M, Nentwich C, Esche E, Repke JU. Adaptation of Dynamic Data‐Driven Models for Real‐Time Applications: From Simulated to Real Batch Distillation Trajectories by Transfer Learning. CHEM-ING-TECH 2023. [DOI: 10.1002/cite.202200228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/01/2023]
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8
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Srinivas SV, Karimi IA. Zone-wise Surrogate Modelling (ZSM) of Univariate Systems. Comput Chem Eng 2023. [DOI: 10.1016/j.compchemeng.2023.108249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
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9
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Zinare T, Di Pretoro A, Chiari V, Montastruc L, Negny S. Benefits of feasibility constrained sampling on unit operations surrogate model accuracy. Comput Chem Eng 2023. [DOI: 10.1016/j.compchemeng.2023.108210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/14/2023]
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10
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Abdullah F, Christofides PD. Data-based modeling and control of nonlinear process systems using sparse identification: An overview of recent results. Comput Chem Eng 2023. [DOI: 10.1016/j.compchemeng.2023.108247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
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11
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Elmisaoui S, Benjelloun S, Chkifa A, Latifi AM. Surrogate model based on hierarchical sparse polynomial interpolation for the phosphate ore dissolution. Comput Chem Eng 2023. [DOI: 10.1016/j.compchemeng.2023.108174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
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12
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Madin OC, Shirts MR. Using physical property surrogate models to perform accelerated multi-fidelity optimization of force field parameters †. DIGITAL DISCOVERY 2023; 2:828-847. [PMCID: PMC10259372 DOI: 10.1039/d2dd00138a] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Accepted: 04/28/2023] [Indexed: 06/14/2023]
Abstract
Accurate representations of van der Waals dispersion–repulsion interactions play an important role in high-quality molecular dynamics simulations. Training the force field parameters used in the Lennard Jones (LJ) potential typically used to represent these interactions is challenging, generally requiring adjustment based on simulations of macroscopic physical properties. The large computational expense of these simulations, especially when many parameters must be trained simultaneously, limits the size of training data set and number of optimization steps that can be taken, often requiring modelers to perform optimizations within a local parameter region. To allow for more global LJ parameter optimization against large training sets, we introduce a multi-fidelity optimization technique which uses Gaussian process surrogate modeling to build inexpensive models of physical properties as a function of LJ parameters. This approach allows for fast evaluation of approximate objective functions, greatly accelerating searches over parameter space and enabling the use of optimization algorithms capable of searching more globally. In this study, we use an iterative framework which performs global optimization with differential evolution at the surrogate level, followed by validation at the simulation level and surrogate refinement. Using this technique on two previously studied training sets, containing up to 195 physical property targets, we refit a subset of the LJ parameters for the OpenFF 1.0.0 (Parsley) force field. We demonstrate that this multi-fidelity technique can find improved parameter sets compared to a purely simulation-based optimization by searching more broadly and escaping local minima. Additionally, this technique often finds significantly different parameter minima that have comparably accurate performance. In most cases, these parameter sets are transferable to other similar molecules in a test set. Our multi-fidelity technique provides a platform for rapid, more global optimization of molecular models against physical properties, as well as a number of opportunities for further refinement of the technique. We present a multi-fidelity method for optimizing nonbonded force field parameters against physical property data. Leveraging fast surrogate models, we accelerate the parameter search and find novel solutions that improve force field performance.![]()
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Affiliation(s)
- Owen C. Madin
- Department of Chemical & Biological Engineering, University of Colorado BoulderBoulderCOUSA80309
| | - Michael R. Shirts
- Department of Chemical & Biological Engineering, University of Colorado BoulderBoulderCOUSA80309
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Matsunami K, Miura T, Yaginuma K, Tanabe S, Badr S, Sugiyama H. Surrogate modeling of dissolution behavior toward efficient design of tablet manufacturing processes. Comput Chem Eng 2023. [DOI: 10.1016/j.compchemeng.2023.108141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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14
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Pham TD, Manapragada C, Rajan N, Aickelin U. Industrial process optimisation: Decision support under high uncertainty using inferred objective function candidates. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.108100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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15
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Laky D, Casas-Orozco D, Laird CD, Reklaitis GV, Nagy ZK. Simulation-optimization framework for the digital design of pharmaceutical processes using Pyomo and PharmaPy. Ind Eng Chem Res 2022; 61:16128-16140. [PMID: 38179037 PMCID: PMC10765421 DOI: 10.1021/acs.iecr.2c01636] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2024]
Abstract
The problem of performing model-based process design and optimization in the pharmaceutical industry is an important and challenging one both computationally and in choice of solution implementation. In this work, a framework is presented to directly utilize a process simulator via callbacks during derivative-based optimization. The framework allows users with little experience in translating mechanistic ODEs and PDEs to robust, fully discretized algebraic formulations, required for executing simultaneous equation-oriented optimization, to obtain mathematically guaranteed optima at a competitive solution time when compared with existing derivative-free and derivative-based frameworks. The effectiveness of the framework in accuracy of optimal solution as well as computational efficiency is analyzed on on two case studies: (i) an integrated 2-unit reaction synthesis train used for the synthesis of an anti-cancer active pharmaceutical ingredient, and (ii) a more complex flowsheet representing a common synthesis-purification-isolation train of a pharmaceutical manufacturing processes.
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Affiliation(s)
- Daniel Laky
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47906, USA
| | - Daniel Casas-Orozco
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47906, USA
| | - Carl D. Laird
- Chemical Engineering Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Gintaras V. Reklaitis
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47906, USA
| | - Zoltan K. Nagy
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47906, USA
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Jedermann R, Lang W. Wrapper Functions for Integrating Mathematical Models into Digital Twin Event Processing. SENSORS (BASEL, SWITZERLAND) 2022; 22:7964. [PMID: 36298315 PMCID: PMC9611674 DOI: 10.3390/s22207964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 10/14/2022] [Accepted: 10/18/2022] [Indexed: 06/16/2023]
Abstract
Analog sensors often require complex mathematical models for data analysis. Digital twins (DTs) provide platforms to display sensor data in real time but still lack generic solutions regarding how mathematical models and algorithms can be integrated. Based on previous tests for monitoring and predicting banana fruit quality along the cool chain, we demonstrate how a system of multiple models can be converted into a DT. Our new approach provides a set of generic "wrapper functions", which largely simplify model integration. The wrappers connect the in- and outputs of models to the streaming platform and, thus, require only minor changes to the model software. Different scenarios for model linking structures are considered, including simultaneous processing of multiple models, sequential processing of life-cycle-specific models, and predictive models, based on data from the current and previous life cycles. The wrapper functions can be easily adapted to host models or microservices from various applications fields, to predict the future system behavior and to test what-if scenarios.
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Kim SH, Landa HOR, Ravutla S, Realff MJ, Boukouvala F. Data-Driven Simultaneous Process Optimization and Adsorbent Selection for Vacuum Pressure Swing Adsorption. Chem Eng Res Des 2022. [DOI: 10.1016/j.cherd.2022.10.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Ma K, Sahinidis NV, Bindlish R, Bury SJ, Haghpanah R, Rajagopalan S. Data-driven strategies for extractive distillation unit optimization. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.107970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Bubel M, Seidel T, Ludl P, Asprion N, Bortz M. Reusable surrogate models for flow sheet simulation. CHEM-ING-TECH 2022. [DOI: 10.1002/cite.202255042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- M. Bubel
- Fraunhofer Institut für Techno‐ und Wirtschaftsmathematik Optimierung Fraunhofer Platz 1 67663 Kaiserslautern Germany
| | - T. Seidel
- Fraunhofer Institut für Techno‐ und Wirtschaftsmathematik Optimierung Fraunhofer Platz 1 67663 Kaiserslautern Germany
| | - P. O. Ludl
- Fraunhofer Institut für Techno‐ und Wirtschaftsmathematik Optimierung Fraunhofer Platz 1 67663 Kaiserslautern Germany
| | - N. Asprion
- BASF SE Chemical and Process Engineering Carl-Bosch-Str. 38 67063 Ludwigshafen am Rhein Germany
| | - M. Bortz
- Fraunhofer Institut für Techno‐ und Wirtschaftsmathematik Optimierung Fraunhofer Platz 1 67663 Kaiserslautern Germany
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22
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A Review of Proxy Modeling Highlighting Applications for Reservoir Engineering. ENERGIES 2022. [DOI: 10.3390/en15145247] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Numerical models can be used for many purposes in oil and gas engineering, such as production optimization and forecasting, uncertainty analysis, history matching, and risk assessment. However, subsurface problems are complex and non-linear, and making reliable decisions in reservoir management requires substantial computational effort. Proxy models have gained much attention in recent years. They are advanced non-linear interpolation tables that can approximate complex models and alleviate computational effort. Proxy models are constructed by running high-fidelity models to gather the necessary data to create the proxy model. Once constructed, they can be a great choice for different tasks such as uncertainty analysis, optimization, forecasting, etc. The application of proxy modeling in oil and gas has had an increasing trend in recent years, and there is no consensus rule on the correct choice of proxy model. As a result, it is crucial to better understand the advantages and disadvantages of various proxy models. The existing work in the literature does not comprehensively cover all proxy model types, and there is a considerable requirement for fulfilling the existing gaps in summarizing the classification techniques with their applications. We propose a novel categorization method covering all proxy model types. This review paper provides a more comprehensive guideline on comparing and developing a proxy model compared to the existing literature. Furthermore, we point out the advantages of smart proxy models (SPM) compared to traditional proxy models (TPM) and suggest how we may further improve SPM accuracy where the literature is limited. This review paper first introduces proxy models and shows how they are classified in the literature. Then, it explains that the current classifications cannot cover all types of proxy models and proposes a novel categorization based on various development strategies. This new categorization includes four groups multi-fidelity models (MFM), reduced-order models (ROM), TPM, and SPM. MFMs are constructed based on simplifying physics assumptions (e.g., coarser discretization), and ROMs are based on dimensional reduction (i.e., neglecting irrelevant parameters). Developing these two models requires an in-depth knowledge of the problem. In contrast, TPMs and novel SPMs require less effort. In other words, they do not solve the complex underlying mathematical equations of the problem; instead, they decouple the mathematical equations into a numeric dataset and train statistical/AI-driven models on the dataset. Nevertheless, SPMs implement feature engineering techniques (i.e., generating new parameters) for its development and can capture the complexities within the reservoir, such as the constraints and characteristics of the grids. The newly introduced parameters can help find the hidden patterns within the parameters, which eventually increase the accuracy of SPMs compared to the TPMs. This review highlights the superiority of SPM over traditional statistical/AI-based proxy models. Finally, the application of various proxy models in the oil and gas industry, especially in subsurface modeling with a set of real examples, is presented. The introduced guideline in this review aids the researchers in obtaining valuable information on the current state of PM problems in the oil and gas industry.
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Ahmad M, Karimi IA. Families of similar surrogate forms based on predictive accuracy and model complexity. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.107845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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Bradley W, Kim J, Kilwein Z, Blakely L, Eydenberg M, Jalvin J, Laird C, Boukouvala F. Perspectives on the Integration between First-Principles and Data-Driven Modeling. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.107898] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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Optimal Therapy Design With Tumor Microenvironment Normalization. AIChE J 2022. [DOI: 10.1002/aic.17747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Multilevel surrogate modeling of an amine scrubbing process for
CO
2
capture. AIChE J 2022. [DOI: 10.1002/aic.17705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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28
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Kinetics of the Direct DME Synthesis: State of the Art and Comprehensive Comparison of Semi-Mechanistic, Data-Based and Hybrid Modeling Approaches. Catalysts 2022. [DOI: 10.3390/catal12030347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
Hybrid kinetic models represent a promising alternative to describe and evaluate the effect of multiple variables in the performance of complex chemical processes, since they combine system knowledge and extrapolability of the (semi-)mechanistic models in a wide range of reaction conditions with the adaptability and fast convergence of data-based approaches (e.g., artificial neural networks—ANNs). For the first time, a hybrid kinetic model for the direct DME synthesis was developed consisting of a reactor model, i.e., balance equations, and an ANN for the reaction kinetics. The accuracy, computational time, interpolation and extrapolation ability of the new hybrid model were compared to those of aumped and a data-based model with the same validity range, using both simulations and experiments. The convergence of parameter estimation and simulations with the hybrid model is much faster than with theumped model, and the predictions show a greater degree of accuracy within the models’ validity range. A satisfactory dimension and range extrapolation was reached when the extrapolated variable was included in the knowledge module of the model. This feature is particularly dependent on the network architecture and phenomena covered by the underlying model, andess on the experimental conditions evaluated during model development.
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Ikonen TJ, Heljanko K, Harjunkoski I. Surrogate‐based optimization of a periodic rescheduling algorithm. AIChE J 2022. [DOI: 10.1002/aic.17656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Teemu J. Ikonen
- Department of Chemical and Metallurgical Engineering Aalto University Aalto Finland
| | - Keijo Heljanko
- Department of Computer Science University of Helsinki Helsinki Finland
- Helsinki Institute for Information Technology (HIIT) Helsinki Finland
| | - Iiro Harjunkoski
- Department of Chemical and Metallurgical Engineering Aalto University Aalto Finland
- Hitachi Energy Research Mannheim Germany
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Thebelt A, Wiebe J, Kronqvist J, Tsay C, Misener R. Maximizing information from chemical engineering data sets: Applications to machine learning. Chem Eng Sci 2022. [DOI: 10.1016/j.ces.2022.117469] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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31
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Di Pretoro A, Bruns B, Negny S, Grünewald M, Riese J. Demand Response Scheduling Using Derivative-Based Dynamic Surrogate Models. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.107711] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Hao Z, Zhang C, Lapkin AA. Efficient Surrogates Construction of Chemical Processes: Case studies on Pressure Swing Adsorption and
Gas‐to‐Liquids. AIChE J 2022. [DOI: 10.1002/aic.17616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Zhimian Hao
- Department of Chemical Engineering and Biotechnology University of Cambridge Cambridge UK
| | - Chonghuan Zhang
- Department of Chemical Engineering and Biotechnology University of Cambridge Cambridge UK
| | - Alexei A. Lapkin
- Department of Chemical Engineering and Biotechnology University of Cambridge Cambridge UK
- Cambridge Centre for Advanced Research and Education in Singapore, CARES Ltd Singapore
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Azadi P, Winz J, Leo E, Klock R, Engell S. A hybrid dynamic model for the prediction of molten iron and slag quality indices of a large-scale blast furnace. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2021.107573] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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35
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Dosta M, Chan TT. Linking process-property relationships for multicomponent agglomerates using DEM-ANN-PBM coupling. POWDER TECHNOL 2022. [DOI: 10.1016/j.powtec.2022.117156] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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36
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Esche E, Weigert J, Brand Rihm G, Göbel J, Repke JU. Architectures for neural networks as surrogates for dynamic systems in chemical engineering. Chem Eng Res Des 2022. [DOI: 10.1016/j.cherd.2021.10.042] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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37
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Schöneberger JC, Aker B, Fricke A. Explaining and Integrating Machine Learning Models with Rigorous Simulation. CHEM-ING-TECH 2021. [DOI: 10.1002/cite.202100089] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
| | - Burcu Aker
- CGC Capital-Gain Consultants GmbH Poststraße 12 10178 Berlin Germany
| | - Armin Fricke
- CGC Capital-Gain Consultants GmbH Poststraße 12 10178 Berlin Germany
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38
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Vázquez D, Guillén-Gosálbez G. Process design within planetary boundaries: Application to CO2 based methanol production. Chem Eng Sci 2021. [DOI: 10.1016/j.ces.2021.116891] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
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Granacher J, Kantor ID, Maréchal F. Increasing Superstructure Optimization Capacity Through Self-Learning Surrogate Models. FRONTIERS IN CHEMICAL ENGINEERING 2021. [DOI: 10.3389/fceng.2021.778876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Simulation-based optimization models are widely applied to find optimal operating conditions of processes. Often, computational challenges arise from model complexity, making the generation of reliable design solutions difficult. We propose an algorithm for replacing non-linear process simulation models integrated in multi-level optimization of a process and energy system superstructure with surrogate models, applying an active learning strategy to continuously enrich the database on which the surrogate models are trained and evaluated. Surrogate models are generated and trained on an initial data set, each featuring the ability to quantify the uncertainty with which a prediction is made. Until a defined prediction quality is met, new data points are continuously labeled and added to the training set. They are selected from a pool of unlabeled data points based on the predicted uncertainty, ensuring a rapid improvement of surrogate quality. When applied in the optimization superstructure, the surrogates can only be used when the prediction quality for the given data point reaches a specified threshold, otherwise the original simulation model is called for evaluating the process performance and the newly obtained data points are used to improve the surrogates. The method is tested on three simulation models, ranging in size and complexity. The proposed approach yields mean squared errors of the test prediction below 2% for all cases. Applying the active learning approach leads to better predictions compared to random sampling for the same size of database. When integrated in the optimization framework, simpler surrogates are favored in over 60% of cases, while the more complex ones are enabled by using simulation results generated during optimization for improving the surrogates after the initial generation. Significant time savings are recorded when using complex process simulations, though the advantage gained for simpler processes is marginal. Overall, we show that the proposed method saves time and adds flexibility to complex superstructure optimization problems that involve optimizing process operating conditions. Computational time can be greatly reduced without penalizing result quality, while the continuous improvement of surrogates when simulation is used in the optimization leads to a natural refinement of the model.
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Janus T, Engell S. Iterative Process Design with Surrogate‐Assisted Global Flowsheet Optimization. CHEM-ING-TECH 2021. [DOI: 10.1002/cite.202100095] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Tim Janus
- TU Dortmund University Process Dynamics and Operations Group Emil-Figge-Straße 70 44137 Dortmund Germany
| | - Sebastian Engell
- TU Dortmund University Process Dynamics and Operations Group Emil-Figge-Straße 70 44137 Dortmund Germany
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42
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Fransen MP, Langelaar M, Schott DL. Application of DEM-based metamodels in bulk handling equipment design: Methodology and DEM case study. POWDER TECHNOL 2021. [DOI: 10.1016/j.powtec.2021.07.048] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Schack D, Lueg L, Schmidt R, von Kurnatowski M, Ludl PO, Bortz M. Data‐Driven Process Simulation Using Connected Surrogate Unit Models Exemplified on a Steam Methane Reforming Process. CHEM-ING-TECH 2021. [DOI: 10.1002/cite.202100087] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Dominik Schack
- AIR LIQUIDE Forschung und Entwicklung GmbH Innovation Campus Frankfurt Gwinnerstrasse 27–33 60388 Frankfurt am Main Germany
| | - Laurens Lueg
- AIR LIQUIDE Forschung und Entwicklung GmbH Innovation Campus Frankfurt Gwinnerstrasse 27–33 60388 Frankfurt am Main Germany
| | - Robin Schmidt
- AIR LIQUIDE Forschung und Entwicklung GmbH Innovation Campus Frankfurt Gwinnerstrasse 27–33 60388 Frankfurt am Main Germany
| | - Martin von Kurnatowski
- Fraunhofer Institute for Industrial Mathematics ITWM Department Optimization – Technical Processes Fraunhofer-Platz 1 67663 Kaiserslautern Germany
| | - Patrick Otto Ludl
- Fraunhofer Institute for Industrial Mathematics ITWM Department Optimization – Technical Processes Fraunhofer-Platz 1 67663 Kaiserslautern Germany
| | - Michael Bortz
- Fraunhofer Institute for Industrial Mathematics ITWM Department Optimization – Technical Processes Fraunhofer-Platz 1 67663 Kaiserslautern Germany
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Schweidtmann AM, Esche E, Fischer A, Kloft M, Repke J, Sager S, Mitsos A. Machine Learning in Chemical Engineering: A Perspective. CHEM-ING-TECH 2021. [DOI: 10.1002/cite.202100083] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Affiliation(s)
- Artur M. Schweidtmann
- Delft University of Technology Department of Chemical Engineering Van der Maasweg 9 2629 HZ Delft The Netherlands
- RWTH Aachen University Aachener Verfahrenstechnik Forckenbeckstr. 51 52074 Aachen Germany
| | - Erik Esche
- Technische Universität Berlin Fachgebiet Dynamik und Betrieb technischer Anlagen Straße des 17. Juni 135 10623 Berlin Germany
| | - Asja Fischer
- Ruhr-Universität Bochum Department of Mathematics Universitätsstraße 150 44801 Bochum Germany
| | - Marius Kloft
- Technische Universität Kaiserslautern Department of Computer Science Erwin-Schrödinger-Straße 52 67663 Kaiserslautern Germany
| | - Jens‐Uwe Repke
- Technische Universität Berlin Fachgebiet Dynamik und Betrieb technischer Anlagen Straße des 17. Juni 135 10623 Berlin Germany
| | - Sebastian Sager
- Otto-von-Guericke-Universität Magdeburg Department of Mathematics Universitätsplatz 2 39106 Magdeburg Germany
| | - Alexander Mitsos
- RWTH Aachen University Aachener Verfahrenstechnik Forckenbeckstr. 51 52074 Aachen Germany
- JARA Center for Simulation and Data Science (CSD) Aachen Germany
- Forschungszentrum Jülich Institute for Energy and Climate Research IEK-10 Energy Systems Engineering Wilhelm-Johnen-Straße 52428 Jülich Germany
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45
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Tsay C. Sobolev trained neural network surrogate models for optimization. Comput Chem Eng 2021. [DOI: 10.1016/j.compchemeng.2021.107419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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46
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Bubel M, Ludl PO, Seidel T, Asprion N, Bortz M. A Modular Approach for Surrogate Modeling of Flowsheets. CHEM-ING-TECH 2021. [DOI: 10.1002/cite.202100077] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Martin Bubel
- Fraunhofer Institute for Industrial Mathematics ITWM Fraunhofer-Platz 1 67663 Kaiserslautern Germany
- Fraunhofer Center for Machine Learning Germany
| | - Patrick Otto Ludl
- Fraunhofer Institute for Industrial Mathematics ITWM Fraunhofer-Platz 1 67663 Kaiserslautern Germany
| | - Tobias Seidel
- Fraunhofer Institute for Industrial Mathematics ITWM Fraunhofer-Platz 1 67663 Kaiserslautern Germany
| | - Norbert Asprion
- Chemical and Process Engineering BASF SE Carl-Bosch-Straße 38 67056 Ludwigshafen Germany
| | - Michael Bortz
- Fraunhofer Institute for Industrial Mathematics ITWM Fraunhofer-Platz 1 67663 Kaiserslautern Germany
- Fraunhofer Center for Machine Learning Germany
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Ahmad M, Karimi IA. Revised learning based evolutionary assistive paradigm for surrogate selection (LEAPS2v2). Comput Chem Eng 2021. [DOI: 10.1016/j.compchemeng.2021.107385] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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48
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Franzoi RE, Kelly JD, Menezes BC, Swartz CL. An adaptive sampling surrogate model building framework for the optimization of reaction systems. Comput Chem Eng 2021. [DOI: 10.1016/j.compchemeng.2021.107371] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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49
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Thebelt A, Kronqvist J, Mistry M, Lee RM, Sudermann-Merx N, Misener R. ENTMOOT: A framework for optimization over ensemble tree models. Comput Chem Eng 2021. [DOI: 10.1016/j.compchemeng.2021.107343] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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50
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Sansana J, Joswiak MN, Castillo I, Wang Z, Rendall R, Chiang LH, Reis MS. Recent trends on hybrid modeling for Industry 4.0. Comput Chem Eng 2021. [DOI: 10.1016/j.compchemeng.2021.107365] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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